# Courses » TIM250 » Spring 2013, Section 01 » Stochastic Optimization in Business Intelligence: Digital Advertising and Online Marketing

TIM 250: Stochastic Optimization in Information Systems and Technology with a special focus on Digital Online Advertising

TIM 250 in brief

TIM 250

Stochastic Optimization in Information Systems and Technology with a special focus on Digital Online Advertising

Dates

April 2, 2013 – May, 2013

Time

Tuesdays 6:00 pm -9:30 pm

Location

Main UCSC campus JB156  and Silicon Valley Campus (SVC) SVC 303

The Silicon Valley Campus is located at 2505 Augustine Drive, Santa Clara, CA. You may find a map and directions at http://www.ucsc-extension.edu/directions

Webpage

https://courses.soe.ucsc.edu/courses/tim250/Spring13/01

Instructors

Dr James G. Shanahan

Independent Consultant

541 Duncan Street, San Francisco CA 94131
James.Shanahan_AT_gmail.com

Office Hours

Dr. James G. Shanahan: (by appointment)

Please send email:  James.Shanahan_AT_gmail.com

with subject TIM 250 Spring 2013

Recorded Lectures

https://webcast.ucsc.edu/

Grading

 Task Value Homework and class participation 30% Exams: midterm (20%) and Final exam 40% Projects: IPinYou Real-time bidding contest ($160,000 prize) 30% Focus Want to win$160,000 for your project work while learning? If yes, read on!

Focus: TIM250 will focus on getting students familiar with core principles in Stochastic Optimization, grounding these principles in both (1) examples taken primarily form online advertising (a $65 Billion industry) and in (2) example projects and code in R and Matlab. Each class will be composed of theory, practice and problems, thereby informing and inspiring students on how to apply theory to practice. The course is being taught a technical leader in the field of online advertising with over 25 years of research and development experience at world renowned research labs such as Xerox, AT&T and Clairvoyance. In addition, the instructor has broader experiences in web search, online advertising, information retrieval, statistical data mining and machine learning. You will learn some of the following skills: • Analyze intelligent support systems for marketing decisions as well as develop mathematical models for optimizing sales, marketing, and pricing decisions in high tech • Learn the core subjects of optimization theory: gradient descent; classical programming; nonlinear programming; and linear programming. See how they are used every day in machine learning and in online advertising. • Learn basics of dynamic programming and Markov decision processes(MDP), including value and policy iteration for finite and infinite horizon situations. Look at applications setting policies for online adverting to optimize various business objectives • Students will get to work on a real-world problem (with real data) in the area of Online advertising, that of predicting real-time bids on behalf of advertisers. Dr. Shanahan is a judge for this competition, which is organized by IPinYou. The competition has a grand prize of$160,000 (and lots of prestige).
• Study online learning, whose core focus is learning in an explore-exploit mode, in a one example at a time; this is key to understanding, analyzing, and stimulating research in the field of learning in a non-stationary environment such as serving digital ads online; study multi-arm bandit approach to ad serving.
• Stochastic Recommenders: review classical approaches to collaborative filtering while also looking at recent developments in the field of stochastic recommenders with applications to ecommerce and online advertising
• Game Theory: auction mechanism design. Time permitting, we will cover game theory and study its application to online advertising auctions. This is an area that has and continues to revolutionize the economic models of online advertising

The course emphasis will be tuned to the class composition and interest.

Prerequisites: Students are expected to be mathematically mature, and to have had prior exposure to undergraduate linear algebra at the level of  MATH21 or  AMS10  and probability/statistics at the level of AMS 131 or MPE 107.

Course Outline

Course Outline on a week-by-week basis (assuming a one 3 hour lecture per week).